Modelling of Renewable Energy Systems

Author

Javier Elío, Liina Sangolt

Description of the course

In this course, students will gain a deep understanding of the fundamental principles related to renewable energy production, including areas like hydrogen production and wind energy. The curriculum integrates theoretical knowledge with hands-on practical calculations, equipping students with the proficiency to model renewable energy production processes effectively.

Students will enhance their expertise in data analysis and modelling, essential skills for the renewable energy sector. They will be capable of conducting resource potential assessments, select optimal installation sites, and performing cost-benefit analyses to optimize renewable energy projects.

Learning outcomes

Knowledge:

  • Understanding renewable energy, including hydrogen and wind energy.

Skills:

  • Proficiency in energy calculations and modelling.
  • Ability to analyse and apply theoretical knowledge.
  • Competency in data collection and analysis for renewable energy projects.

General Competences:

  • Effective modelling and academic presentation.
  • Strong teamwork and time management skills.
  • Identifying solutions in renewable energy and contributing to professional discussions.

Software

The data have been analysed with R (version 4.3.2) and Rstudio, and the book has been created with Quarto. You may need to install the following packages:

R session

R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: Europe/Oslo
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.3.2    fastmap_1.2.0     cli_3.6.3        
 [5] htmltools_0.5.8.1 tools_4.3.2       rstudioapi_0.17.1 yaml_2.3.10      
 [9] rmarkdown_2.29    knitr_1.49        jsonlite_1.8.9    xfun_0.50        
[13] digest_0.6.37     rlang_1.1.5       renv_1.1.0        evaluate_1.0.3

Spatial projection

We use the CRS suggested by Geonorge. In this sense, the national wide data are in “ETRS89 / UTM zone 33N”, which correspond to the EPSG code of 25833 (link), and therefore we transform all our coordinates to that CRS.

Acknowledgements

This work has been financed by XXX